q27 clustering and climate beliefs
Katz
9/6/2020
## # A tibble: 19 x 2
## Q29 n
## <dbl> <int>
## 1 13 889
## 2 NA 735
## 3 5 654
## 4 4 557
## 5 8 347
## 6 3 180
## 7 11 173
## 8 16 151
## 9 10 128
## 10 7 119
## 11 17 111
## 12 1 84
## 13 12 71
## 14 18 69
## 15 6 53
## 16 2 21
## 17 14 13
## 18 9 6
## 19 15 3## # A tibble: 5 x 2
## Q32 n
## <dbl> <int>
## 1 1 963
## 2 2 1063
## 3 3 1068
## 4 4 673
## 5 NA 597## # A tibble: 5 x 2
## Q32 n
## <dbl> <int>
## 1 1 71
## 2 2 75
## 3 3 74
## 4 4 74
## 5 NA 441## # A tibble: 13 x 2
## Q33 n
## <dbl> <int>
## 1 1 925
## 2 2 60
## 3 3 141
## 4 4 854
## 5 5 60
## 6 6 67
## 7 7 65
## 8 8 243
## 9 9 399
## 10 10 393
## 11 11 221
## 12 12 368
## 13 NA 568## # A tibble: 13 x 2
## Q33 n
## <dbl> <int>
## 1 1 869
## 2 4 789
## 3 10 374
## 4 9 367
## 5 12 332
## 6 8 228
## 7 11 196
## 8 NA 130
## 9 3 117
## 10 6 61
## 11 5 57
## 12 7 56
## 13 2 53## # A tibble: 6 x 2
## Q37 n
## <dbl> <int>
## 1 1 2833
## 2 2 954
## 3 3 28
## 4 4 44
## 5 1234 1
## 6 NA 504Drop NAs for specific questions and filter out disciplines with fewer than 30 (the cutoff) students in sample
## # A tibble: 18 x 2
## Q29 n
## <dbl> <int>
## 1 13 622
## 2 5 486
## 3 4 398
## 4 8 241
## 5 16 120
## 6 3 111
## 7 11 111
## 8 7 95
## 9 10 95
## 10 17 81
## 11 1 70
## 12 12 57
## 13 18 42
## 14 6 34
## 15 2 16
## 16 14 8
## 17 9 3
## 18 15 1## # A tibble: 14 x 2
## Q29 n
## <dbl> <int>
## 1 13 622
## 2 5 486
## 3 4 398
## 4 8 241
## 5 16 120
## 6 3 111
## 7 11 111
## 8 7 95
## 9 10 95
## 10 17 81
## 11 1 70
## 12 12 57
## 13 18 42
## 14 6 34Major counts and percentages
| Q29 | major | n | pct_total | cumulat_pct |
|---|---|---|---|---|
| 13 | Mec | 622 | 24.27 | 24.27 |
| 5 | Che | 486 | 18.96 | 43.23 |
| 4 | Civ | 398 | 15.53 | 58.76 |
| 8 | Ele | 241 | 9.40 | 68.16 |
| 16 | Softw | 120 | 4.68 | 72.84 |
| 3 | Bio | 111 | 4.33 | 77.18 |
| 11 | Ind | 111 | 4.33 | 81.51 |
| 7 | Comp | 95 | 3.71 | 85.21 |
| 10 | Env/Eco | 95 | 3.71 | 88.92 |
| 17 | Str/Arc | 81 | 3.16 | 92.08 |
| 1 | Aer/Oce | 70 | 2.73 | 94.81 |
| 12 | Mat | 57 | 2.22 | 97.03 |
| 18 | Gen | 42 | 1.64 | 98.67 |
| 6 | Con | 34 | 1.33 | 100.00 |
Gender counts overall
| Q37 | n | pct_total |
|---|---|---|
| 1 | 1882 | 73.43 |
| 2 | 646 | 25.20 |
| 3 | 14 | 0.55 |
| 4 | 21 | 0.82 |
fill in 0s for NAs for specific items (Q1, Q3, Q5)
Drop majors with low counts (below 30 students in sample)
## # A tibble: 14 x 2
## Q29 n
## <dbl> <int>
## 1 13 596
## 2 5 469
## 3 4 390
## 4 8 238
## 5 16 118
## 6 3 110
## 7 11 108
## 8 7 93
## 9 10 90
## 10 17 78
## 11 1 70
## 12 12 55
## 13 18 40
## 14 6 31## # A tibble: 14 x 2
## major n
## <chr> <int>
## 1 Mec 596
## 2 Che 469
## 3 Civ 390
## 4 Ele 238
## 5 Softw 118
## 6 Bio 110
## 7 Ind 108
## 8 Comp 93
## 9 Env/Eco 90
## 10 Str/Arc 78
## 11 Aer/Oce 70
## 12 Mat 55
## 13 Gen 40
## 14 Con 31Drop majors with NA as major
## # A tibble: 14 x 2
## major n
## <chr> <int>
## 1 Mec 596
## 2 Che 469
## 3 Civ 390
## 4 Ele 238
## 5 Softw 118
## 6 Bio 110
## 7 Ind 108
## 8 Comp 93
## 9 Env/Eco 90
## 10 Str/Arc 78
## 11 Aer/Oce 70
## 12 Mat 55
## 13 Gen 40
## 14 Con 31Clustering Process (two-step process using UMAP + HDBSCAN)
First perform dimension reduction using UMAP
## NULL## [,1] [,2]
## [1,] -38.02495 -6.968480
## [2,] -31.19541 -3.773391
## [3,] 56.61304 5.437927## [1] -38.02495 -31.19541 56.61304 56.44075 -18.30809 56.84714## [1] -6.968480 -3.773391 5.437927 5.263140 22.195229 5.086338
Next, perform clustering with HDBSCAN
## HDBSCAN clustering for 2486 objects.
## Parameters: minPts = 120
## The clustering contains 6 cluster(s) and 103 noise points.
##
## 0 1 2 3 4 5 6
## 103 1026 173 549 150 348 137
##
## Available fields: cluster, minPts, cluster_scores, membership_prob,
## outlier_scores, hc
Join the dataframes back together again
## # A tibble: 7 x 2
## cluster n
## <dbl> <int>
## 1 1 1026
## 2 3 549
## 3 5 348
## 4 2 173
## 5 4 150
## 6 6 137
## 7 0 103Views of when climate change will affect different groups broken down by cluster assignments
Looking for patterns in the clusters
Understanding cluster compositions more
First create a dataframe with the rankings for each cluster, where a lower ranking means students in the cluster think climate change will affect more categories sooner.
## # A tibble: 7 x 3
## cluster_time_rank cluster cluster_avg
## <int> <dbl> <dbl>
## 1 1 1 0.377
## 2 2 0 3.08
## 3 3 4 3.83
## 4 4 5 5.14
## 5 5 6 7.47
## 6 6 3 9.24
## 7 7 2 17.4Set clustering colors for all plots
Look at distribution of cluster for how when they think each community may be affected by global warming
Same information but faceted with clusters
** This is a good plot for seeing that a cluster’s beliefs about effects of global warming on different populations at different times vary in a clear pattern 
Overall count for clusters
Broken plot
Q5 career topic interests for all ten topics in Q5 by cluster (instead of major)
Table of average number of topics that students in each cluster identified
## # A tibble: 7 x 3
## cluster_time_rank n avg_Q5_total
## <int> <int> <dbl>
## 1 1 1026 3.68
## 2 2 103 3.22
## 3 5 137 3.18
## 4 3 150 3.17
## 5 4 348 2.97
## 6 6 549 2.68
## 7 7 173 2.08Number of Q5 topics identified in each cluster
Proportions of each cluster interested in Q5 topics
Interests within each cluster
## # A tibble: 10 x 6
## # Groups: Q5_item [10]
## Q5_item Q5_item_name statistic p.value parameter method
## <chr> <chr> <dbl> <dbl> <int> <chr>
## 1 Q5a Energy (supply/deman~ 9.35 1.55e- 1 6 Pearson's Chi-squ~
## 2 Q5b Disease 5.14 5.26e- 1 6 Pearson's Chi-squ~
## 3 Q5c Poverty and wealth d~ 52.0 1.88e- 9 6 Pearson's Chi-squ~
## 4 Q5d Climate change 190. 2.46e-38 6 Pearson's Chi-squ~
## 5 Q5e Terrorism and war 11.4 7.58e- 2 6 Pearson's Chi-squ~
## 6 Q5f Water supply 27.9 1.00e- 4 6 Pearson's Chi-squ~
## 7 Q5g Food availability 28.5 7.41e- 5 6 Pearson's Chi-squ~
## 8 Q5h Opp. for future gen 12.4 5.41e- 2 6 Pearson's Chi-squ~
## 9 Q5i Opp. for women and/o~ 88.0 7.97e-17 6 Pearson's Chi-squ~
## 10 Q5j Environmental degrad~ 111. 1.44e-21 6 Pearson's Chi-squ~Chi square tests of whether different clusters (students with different temporal discounting patterns of when climate change will affect various groups) want to address climate-related outcomes in their careers
Q5a - energy (supply or demand)
##
## 0 1
## 1 395 631
## 2 42 61
## 3 72 78
## 4 157 191
## 5 60 77
## 6 218 331
## 7 75 98##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 9.3471, df = 6, p-value = 0.155
Q5d - climate change
##
## 0 1
## 1 551 475
## 2 67 36
## 3 90 60
## 4 235 113
## 5 87 50
## 6 444 105
## 7 164 9##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 190.07, df = 6, p-value < 2.2e-16
alternative Q5d visualization for proportions of each cluster interested in addressing climate change
Q5f - water supply
##
## 0 1
## 1 601 425
## 2 64 39
## 3 91 59
## 4 233 115
## 5 89 48
## 6 379 170
## 7 127 46##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 27.856, df = 6, p-value = 1e-04
Q5j - environmental degradation
##
## 0 1
## 1 552 474
## 2 62 41
## 3 86 64
## 4 226 122
## 5 79 58
## 6 403 146
## 7 151 22##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 110.72, df = 6, p-value < 2.2e-16
##
## 1 3 4 5 6 7 8 10 11 12 13 16 17 18
## 0 3 9 19 22 1 0 3 1 3 1 27 6 6 2
## 1 29 44 165 183 8 33 95 55 50 27 243 46 31 17
## 2 3 2 25 29 3 8 24 2 8 1 50 9 5 4
## 3 12 23 76 114 9 26 60 5 19 12 135 35 17 6
## 4 5 5 23 37 1 4 9 11 9 2 30 9 3 2
## 5 13 16 57 65 6 15 36 13 11 8 81 9 12 6
## 6 5 11 25 19 3 7 11 3 8 4 30 4 4 3##
## Pearson's Chi-squared test
##
## data: cont_table_Q29
## X-squared = 107.65, df = 78, p-value = 0.0147
Chi square tests of whether different clusters (students with different temporal discounting patterns of when climate change will affect various groups) want to pursue various degree options
## # A tibble: 17,402 x 5
## student_id major cluster_time_rank Q3_item Q3_resp
## <int> <chr> <int> <chr> <dbl>
## 1 1 Ele 5 Q3a 0
## 2 1 Ele 5 Q3b 1
## 3 1 Ele 5 Q3c 0
## 4 1 Ele 5 Q3d 0
## 5 1 Ele 5 Q3e 0
## 6 1 Ele 5 Q3f 0
## 7 1 Ele 5 Q3g 0
## 8 2 Ele 4 Q3a 0
## 9 2 Ele 4 Q3b 1
## 10 2 Ele 4 Q3c 0
## # ... with 17,392 more rowsInterest of each topic
Interest within each cluster
Chi square tests of degree interests by cluster – not expecting any differences across clusters
## # A tibble: 7 x 6
## # Groups: Q3_item [7]
## Q3_item Q3_item_name statistic p.value parameter method
## <chr> <chr> <dbl> <dbl> <int> <chr>
## 1 Q3a MA/MS (non-eng) 15.6 0.0164 6 Pearson's Chi-squared test
## 2 Q3b ME/MS (eng) 15.3 0.0183 6 Pearson's Chi-squared test
## 3 Q3c PhD (eng) 7.45 0.281 6 Pearson's Chi-squared test
## 4 Q3d MBA 5.53 0.478 6 Pearson's Chi-squared test
## 5 Q3e JD (law) 12.4 0.0545 6 Pearson's Chi-squared test
## 6 Q3f MD 3.96 0.682 6 Pearson's Chi-squared test
## 7 Q3g Other 11.0 0.0869 6 Pearson's Chi-squared testKruskal wallis tests of whether different clusters (students with different temporal discounting patterns of when climate change will affect various groups) consider various career satisfaction factors important
Career satisfaction items (Q4 items)
## # A tibble: 39,776 x 5
## student_id major cluster_time_rank Q4_item Q4_resp
## <int> <chr> <int> <chr> <dbl>
## 1 1 Ele 5 Q4a 4
## 2 1 Ele 5 Q4b 3
## 3 1 Ele 5 Q4c 3
## 4 1 Ele 5 Q4d 2
## 5 1 Ele 5 Q4e 4
## 6 1 Ele 5 Q4f 2
## 7 1 Ele 5 Q4g 2
## 8 1 Ele 5 Q4h 4
## 9 1 Ele 5 Q4i 4
## 10 1 Ele 5 Q4j 1
## # ... with 39,766 more rows
visualize with boxplots instead of mosaicplots
Table of chi square test results
## # A tibble: 16 x 6
## # Groups: Q4_item [16]
## Q4_item Q4_item_name statistic p.value parameter method
## <chr> <chr> <dbl> <dbl> <int> <chr>
## 1 Q4a Make money 24.3 4.44e-1 24 Pearson's Chi-sq~
## 2 Q4b Fame 25.4 3.82e-1 24 Pearson's Chi-sq~
## 3 Q4c Help others 56.0 2.32e-4 24 Pearson's Chi-sq~
## 4 Q4d Supervise others 25.6 3.71e-1 24 Pearson's Chi-sq~
## 5 Q4e Job sec. and opp. 45.9 4.57e-3 24 Pearson's Chi-sq~
## 6 Q4f Work w/ people 54.0 4.27e-4 24 Pearson's Chi-sq~
## 7 Q4g Invent/design 28.2 2.53e-1 24 Pearson's Chi-sq~
## 8 Q4h Develop knowledge/~ 24.3 4.44e-1 24 Pearson's Chi-sq~
## 9 Q4i Personal/fam. time 31.5 1.40e-1 24 Pearson's Chi-sq~
## 10 Q4j Easy job 68.7 3.41e-6 24 Pearson's Chi-sq~
## 11 Q4k Exciting env. 32.8 1.09e-1 24 Pearson's Chi-sq~
## 12 Q4l Solve societal pro~ 90.5 1.19e-9 24 Pearson's Chi-sq~
## 13 Q4m Use talent/abiliti~ 41.0 1.68e-2 24 Pearson's Chi-sq~
## 14 Q4n Do hands-on work 25.7 3.69e-1 24 Pearson's Chi-sq~
## 15 Q4o Apply math/sci. 23.5 4.90e-1 24 Pearson's Chi-sq~
## 16 Q4p Volunteer w/ chari~ 70.6 1.76e-6 24 Pearson's Chi-sq~Using kruskal-wallis instead of chi square since outcome is Likert-scale item (i.e., ordinal variable)
## # A tibble: 16 x 6
## # Groups: Q4_item [16]
## Q4_item Q4_item_name statistic p.value parameter method
## <chr> <chr> <dbl> <dbl> <int> <chr>
## 1 Q4a Make money 11.9 1.78e- 2 4 Kruskal-Wallis rank~
## 2 Q4b Fame 2.87 5.80e- 1 4 Kruskal-Wallis rank~
## 3 Q4c Help others 25.8 3.52e- 5 4 Kruskal-Wallis rank~
## 4 Q4d Supervise others 2.72 6.05e- 1 4 Kruskal-Wallis rank~
## 5 Q4e Job sec. and opp. 12.0 1.73e- 2 4 Kruskal-Wallis rank~
## 6 Q4f Work w/ people 3.97 4.11e- 1 4 Kruskal-Wallis rank~
## 7 Q4g Invent/design 1.30 8.61e- 1 4 Kruskal-Wallis rank~
## 8 Q4h Develop knowledge/~ 9.42 5.14e- 2 4 Kruskal-Wallis rank~
## 9 Q4i Personal/fam. time 2.69 6.11e- 1 4 Kruskal-Wallis rank~
## 10 Q4j Easy job 4.58 3.34e- 1 4 Kruskal-Wallis rank~
## 11 Q4k Exciting env. 14.6 5.63e- 3 4 Kruskal-Wallis rank~
## 12 Q4l Solve societal pro~ 72.0 8.48e-15 4 Kruskal-Wallis rank~
## 13 Q4m Use talent/abiliti~ 19.6 6.07e- 4 4 Kruskal-Wallis rank~
## 14 Q4n Do hands-on work 4.84 3.04e- 1 4 Kruskal-Wallis rank~
## 15 Q4o Apply math/sci. 0.555 9.68e- 1 4 Kruskal-Wallis rank~
## 16 Q4p Volunteer w/ chari~ 27.0 2.00e- 5 4 Kruskal-Wallis rank~Question 2 items (Working in various sectors) and climate change impacts cluster
## # A tibble: 13,762 x 5
## student_id major cluster_time_rank Q2_item Q2_resp
## <int> <chr> <int> <chr> <dbl>
## 1 1 Ele 5 Q2a 4
## 2 1 Ele 5 Q2b 0
## 3 1 Ele 5 Q2c 4
## 4 1 Ele 5 Q2d 2
## 5 1 Ele 5 Q2e 2
## 6 1 Ele 5 Q2f 2
## 7 1 Ele 5 Q2g 2
## 8 2 Ele 4 Q2a 4
## 9 2 Ele 4 Q2b 1
## 10 2 Ele 4 Q2c 2
## # ... with 13,752 more rows
Using kruskal-wallis instead of chi square since outcome is Likert-scale item (i.e., ordinal variable)
visualize with boxplots instead of mosaicplots
Table of kruskal wallis test results
## # A tibble: 7 x 6
## # Groups: Q2_item [7]
## Q2_item Q2_item_name statistic p.value parameter method
## <chr> <chr> <dbl> <dbl> <int> <chr>
## 1 Q2a Private/Corporate 40.9 0.0170 24 Pearson's Chi-squa~
## 2 Q2b Non-profit/NGO 58.1 0.000117 24 Pearson's Chi-squa~
## 3 Q2c Gov./Public Policy 20.9 0.647 24 Pearson's Chi-squa~
## 4 Q2d Education 39.3 0.0253 24 Pearson's Chi-squa~
## 5 Q2e Entrepreneurship/Sta~ 32.1 0.123 24 Pearson's Chi-squa~
## 6 Q2f Healthcare 20.4 0.673 24 Pearson's Chi-squa~
## 7 Q2g Other 24.1 0.457 24 Pearson's Chi-squa~End of career interest section
Cluster beliefs about global warming
The following series of analyses look at differences among the temporal discounting clusters regarding various beliefs about global warming and climate change
Q18k - We should be taking stronger actions to address climate change
##
## 0 1
## 1 144 861
## 2 25 76
## 3 27 119
## 4 80 263
## 5 41 95
## 6 231 306
## 7 134 36##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 382.38, df = 6, p-value < 2.2e-16
Plot for Major by Q20a
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 2466
##
##
## | climate_df$Q20a_bin
## climate_df$Q29 | 0 | 1 | Row Total |
## ---------------|-----------|-----------|-----------|
## 1 | 6 | 64 | 70 |
## | 4.245 | 1.023 | |
## | 0.086 | 0.914 | 0.028 |
## | 0.013 | 0.032 | |
## | 0.002 | 0.026 | |
## ---------------|-----------|-----------|-----------|
## 3 | 17 | 92 | 109 |
## | 0.822 | 0.198 | |
## | 0.156 | 0.844 | 0.044 |
## | 0.035 | 0.046 | |
## | 0.007 | 0.037 | |
## ---------------|-----------|-----------|-----------|
## 4 | 67 | 319 | 386 |
## | 0.849 | 0.205 | |
## | 0.174 | 0.826 | 0.157 |
## | 0.140 | 0.161 | |
## | 0.027 | 0.129 | |
## ---------------|-----------|-----------|-----------|
## 5 | 83 | 382 | 465 |
## | 0.594 | 0.143 | |
## | 0.178 | 0.822 | 0.189 |
## | 0.173 | 0.192 | |
## | 0.034 | 0.155 | |
## ---------------|-----------|-----------|-----------|
## 6 | 10 | 21 | 31 |
## | 2.629 | 0.634 | |
## | 0.323 | 0.677 | 0.013 |
## | 0.021 | 0.011 | |
## | 0.004 | 0.009 | |
## ---------------|-----------|-----------|-----------|
## 7 | 16 | 76 | 92 |
## | 0.196 | 0.047 | |
## | 0.174 | 0.826 | 0.037 |
## | 0.033 | 0.038 | |
## | 0.006 | 0.031 | |
## ---------------|-----------|-----------|-----------|
## 8 | 60 | 177 | 237 |
## | 4.236 | 1.021 | |
## | 0.253 | 0.747 | 0.096 |
## | 0.125 | 0.089 | |
## | 0.024 | 0.072 | |
## ---------------|-----------|-----------|-----------|
## 10 | 6 | 84 | 90 |
## | 7.541 | 1.818 | |
## | 0.067 | 0.933 | 0.036 |
## | 0.013 | 0.042 | |
## | 0.002 | 0.034 | |
## ---------------|-----------|-----------|-----------|
## 11 | 19 | 87 | 106 |
## | 0.123 | 0.030 | |
## | 0.179 | 0.821 | 0.043 |
## | 0.040 | 0.044 | |
## | 0.008 | 0.035 | |
## ---------------|-----------|-----------|-----------|
## 12 | 8 | 47 | 55 |
## | 0.674 | 0.162 | |
## | 0.145 | 0.855 | 0.022 |
## | 0.017 | 0.024 | |
## | 0.003 | 0.019 | |
## ---------------|-----------|-----------|-----------|
## 13 | 141 | 449 | 590 |
## | 6.080 | 1.466 | |
## | 0.239 | 0.761 | 0.239 |
## | 0.294 | 0.226 | |
## | 0.057 | 0.182 | |
## ---------------|-----------|-----------|-----------|
## 16 | 24 | 93 | 117 |
## | 0.071 | 0.017 | |
## | 0.205 | 0.795 | 0.047 |
## | 0.050 | 0.047 | |
## | 0.010 | 0.038 | |
## ---------------|-----------|-----------|-----------|
## 17 | 15 | 63 | 78 |
## | 0.002 | 0.000 | |
## | 0.192 | 0.808 | 0.032 |
## | 0.031 | 0.032 | |
## | 0.006 | 0.026 | |
## ---------------|-----------|-----------|-----------|
## 18 | 7 | 33 | 40 |
## | 0.076 | 0.018 | |
## | 0.175 | 0.825 | 0.016 |
## | 0.015 | 0.017 | |
## | 0.003 | 0.013 | |
## ---------------|-----------|-----------|-----------|
## Column Total | 479 | 1987 | 2466 |
## | 0.194 | 0.806 | |
## ---------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 34.91984 d.f. = 13 p = 0.0008710119
##
##
## ##
## 0 1
## 1 77 941
## 2 15 87
## 3 17 132
## 4 42 304
## 5 21 114
## 6 173 373
## 7 134 36##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 547.76, df = 6, p-value < 2.2e-16
## Q5d
## major 0 1
## Aer/Oce 49 21
## Bio 100 10
## Che 286 183
## Civ 249 141
## Comp 71 22
## Con 24 7
## Ele 176 62
## Env/Eco 22 68
## Gen 19 21
## Ind 83 25
## Mat 33 22
## Mec 377 219
## Softw 101 17
## Str/Arc 48 30Plot for Major by Q20b
##
## 0 1
## 1 168 849
## 2 26 76
## 3 29 120
## 4 80 266
## 5 37 98
## 6 241 305
## 7 144 26##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 403.53, df = 6, p-value < 2.2e-16
Plot for Major by Q20d
##
## 0 1
## 1 312 706
## 2 49 52
## 3 73 76
## 4 151 195
## 5 75 60
## 6 361 185
## 7 154 15##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 326.39, df = 6, p-value < 2.2e-16
Q22 - What percentage of climate scientists think that human-caused global warming is happening?
##
## 0 1
## 1 484 542
## 2 45 58
## 3 63 87
## 4 160 188
## 5 70 67
## 6 357 192
## 7 134 39##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 105.29, df = 6, p-value < 2.2e-16
Plot for Q23 
Alternative plot for Q23 scores using jitter 
Differences in how to slow down climate change by cluster
Initial plot for Q24
Alternative plot for Q24
Q25 Which of the following…
- is the most abundant greenhouse gas
- amplifies the greenhouse gas effect the most?
- should we be most concerned about when thinking about global warming? CO2
Q25a
##
## 0 1
## 1 867 112
## 2 87 13
## 3 121 21
## 4 282 45
## 5 115 12
## 6 459 63
## 7 137 24##
## Pearson's Chi-squared test
##
## data: cont_table_Q25a
## X-squared = 4.1767, df = 6, p-value = 0.6528
Q25b
##
## 0 1
## 1 582 397
## 2 52 48
## 3 75 67
## 4 193 134
## 5 76 51
## 6 379 143
## 7 121 40##
## Pearson's Chi-squared test
##
## data: cont_table_Q25b
## X-squared = 50.155, df = 6, p-value = 4.376e-09
Q25c
##
## 0 1
## 1 427 552
## 2 36 64
## 3 61 81
## 4 142 185
## 5 56 71
## 6 266 256
## 7 104 57##
## Pearson's Chi-squared test
##
## data: cont_table_Q25c
## X-squared = 35.308, df = 6, p-value = 3.756e-06
Q26 - How much do you agree or disagree with the following statements about Earth’s climate?
Recoding Q26 for t/F and then creating a total score out of 8
Initial plot for Q26
Alternative plot for Q26 using geom_jitter
Q28 (global warming as technical or social issue) differences by cluster assignment (boxplots and ANOVA results)
Using geom_jitter
ANOVA for Q27 clusters and Q28 averaged social score
## Df Sum Sq Mean Sq F value Pr(>F)
## as_factor(cluster_time_rank) 6 385.3 64.21 66.95 <2e-16 ***
## Residuals 2270 2177.0 0.96
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Q28_social_norm ~ as_factor(cluster_time_rank), data = climate_df)
##
## $`as_factor(cluster_time_rank)`
## diff lwr upr p adj
## 2-1 -0.26507259 -0.5762095 0.046064330 0.1545843
## 3-1 -0.38805376 -0.6523548 -0.123752687 0.0003088
## 4-1 -0.32761949 -0.5153428 -0.139896153 0.0000059
## 5-1 -0.77319411 -1.0503109 -0.496077358 0.0000000
## 6-1 -0.65960388 -0.8188736 -0.500334155 0.0000000
## 7-1 -1.47044829 -1.7182793 -1.222617258 0.0000000
## 3-2 -0.12298118 -0.5088216 0.262859295 0.9659283
## 4-2 -0.06254690 -0.4005674 0.275473602 0.9981250
## 5-2 -0.50812152 -0.9028512 -0.113391863 0.0028423
## 6-2 -0.39453130 -0.7176174 -0.071445153 0.0059224
## 7-2 -1.20537570 -1.5801263 -0.830625079 0.0000000
## 4-3 0.06043427 -0.2350429 0.355911410 0.9967199
## 5-3 -0.38514035 -0.7441116 -0.026169065 0.0261933
## 6-3 -0.27155012 -0.5498189 0.006718695 0.0611465
## 7-3 -1.08239453 -1.4192727 -0.745516348 0.0000000
## 5-4 -0.44557462 -0.7525687 -0.138580524 0.0003843
## 6-4 -0.33198440 -0.5389108 -0.125058012 0.0000479
## 7-4 -1.14282880 -1.4236702 -0.861987383 0.0000000
## 6-5 0.11359022 -0.1768787 0.404059106 0.9109826
## 7-5 -0.69725418 -1.0442780 -0.350230342 0.0000001
## 7-6 -0.81084440 -1.0735204 -0.548168374 0.0000000
Using geom_jitter instead
ANOVA for Q27 clusters and Q28 averaged technical score
## Df Sum Sq Mean Sq F value Pr(>F)
## as_factor(cluster_time_rank) 6 655.9 109.32 202.8 <2e-16 ***
## Residuals 2270 1223.5 0.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Q28_tech_norm ~ as_factor(cluster_time_rank), data = climate_df)
##
## $`as_factor(cluster_time_rank)`
## diff lwr upr p adj
## 2-1 -0.161184911 -0.3944357 0.07206587 0.3900278
## 3-1 -0.113060952 -0.3112002 0.08507829 0.6269918
## 4-1 -0.118775259 -0.2595063 0.02195576 0.1630589
## 5-1 -0.238820056 -0.4465669 -0.03107326 0.0124850
## 6-1 -0.579112867 -0.6985130 -0.45971274 0.0000000
## 7-1 -2.100046215 -2.2858383 -1.91425412 0.0000000
## 3-2 0.048123960 -0.2411300 0.33737793 0.9989729
## 4-2 0.042409652 -0.2109950 0.29581432 0.9989377
## 5-2 -0.077635145 -0.3735531 0.21828281 0.9873821
## 6-2 -0.417927956 -0.6601367 -0.17571917 0.0000080
## 7-2 -1.938861304 -2.2198015 -1.65792108 0.0000000
## 4-3 -0.005714308 -0.2272254 0.21579677 1.0000000
## 5-3 -0.125759104 -0.3948700 0.14335178 0.8131735
## 6-3 -0.466051916 -0.6746624 -0.25744145 0.0000000
## 7-3 -1.986985264 -2.2395336 -1.73443697 0.0000000
## 5-4 -0.120044797 -0.3501898 0.11010022 0.7209814
## 6-4 -0.460337608 -0.6154646 -0.30521060 0.0000000
## 7-4 -1.981270956 -2.1918100 -1.77073188 0.0000000
## 6-5 -0.340292811 -0.5580493 -0.12253629 0.0000861
## 7-5 -1.861226159 -2.1213804 -1.60107194 0.0000000
## 7-6 -1.520933348 -1.7178543 -1.32401236 0.0000000